Robots may be programmed to perform a variety of tasks such as, for example, autonomous or semi-autonomous navigation, manipulating objects (e.g., repositioning an object, altering an object, and/or picking up an object and moving it to a different location), transporting objects (without necessarily manipulating those objects), monitoring environmental conditions, functioning as “video conferencing on wheels”, and so forth.
If a robot does not know where it is, it can be difficult for the robot to perform various tasks with accuracy. Robot localization seeks to resolve the pose (location and orientation) of a robot in an environment, thereby enabling the robot to know where it is. Various robot localization techniques have been proposed. For example, dead reckoning uses sensor readings from one or more odometers of a robot, along with trigonometry and kinematic equations, to determine how a robot has moved and to update the pose of the robot based on the determination. Also, for example, some robot localization techniques utilize active beacons (e.g., infrared, sound) and/or passive beacons (e.g., reflectors) with known locations throughout an environment to enable sensor(s) of a robot to sense those beacons and triangulate or otherwise determine the robot's pose relative to the sensed beacons and, as a result, its pose in the environment. These and/or other robot localization techniques may suffer from one or more drawbacks such as localization accuracy, computational costs, costs associated with placing beacons or other landmarks in the environment, and/or costs associated with sensor(s) necessary to perform the localization techniques. Additional and/or alternative drawbacks of the aforementioned techniques and/or other techniques may be presented.